analyze_player_data.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read games data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = pd.read_csv('nba_games/games.csv')\n",
    "\n",
    "# filter to the last 2 seasons\n",
    "games = games[games['SEASON'] > 2020]\n",
    "games['GAME_DATE_EST'] = pd.to_datetime(games['GAME_DATE_EST'])\n",
    "\n",
    "# build day of year, week of year, and month of year\n",
    "for idx, row in games.iterrows():\n",
    "    games.loc[idx, 'DAY'] = row['GAME_DATE_EST'].timetuple().tm_yday\n",
    "    games.loc[idx, 'WEEK'] = row['GAME_DATE_EST'].isocalendar().week\n",
    "    games.loc[idx, 'MONTH'] = row['GAME_DATE_EST'].month\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read game details set and merge with games set. Define separaet dfs for home team and visiting team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j4/xht4swnx3v38mbt0w75bjqs40000gn/T/ipykernel_79402/3113203320.py:1: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  game_details = pd.read_csv('nba_games/games_details.csv')\n"
     ]
    }
   ],
   "source": [
    "game_details = pd.read_csv('nba_games/games_details.csv')\n",
    "df_home = pd.merge(game_details, games, left_on=[\"GAME_ID\", \"TEAM_ID\"], right_on=['GAME_ID', \"HOME_TEAM_ID\"], how=\"inner\")\n",
    "df_visit = pd.merge(game_details, games, left_on=[\"GAME_ID\", \"TEAM_ID\"], right_on=['GAME_ID', \"VISITOR_TEAM_ID\"], how=\"inner\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for nulls and find minimum number of players per team, minimum number of players to start a NBA game is 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [(PLAYER_ID, count)]\n",
      "Index: []\n",
      "PLAYER_ID  count    10\n",
      "dtype: int64\n",
      "Empty DataFrame\n",
      "Columns: [(PLAYER_ID, count)]\n",
      "Index: []\n",
      "PLAYER_ID  count    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "player_teams_df = df_home.groupby(['GAME_ID', 'TEAM_ID']).agg({'PLAYER_ID': ['count']})\n",
    "print(player_teams_df[player_teams_df.isnull().any(axis=1)])\n",
    "print(player_teams_df.min())\n",
    "\n",
    "player_teams_df = df_visit.groupby(['GAME_ID', 'TEAM_ID']).agg({'PLAYER_ID': ['count']})\n",
    "print(player_teams_df[player_teams_df.isnull().any(axis=1)])\n",
    "print(player_teams_df.min())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate seconds played per player per game per team.\n",
    "Not using score because some players are important to the game through assists and such instead of scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_seconds_played(df):\n",
    "    for idx, row in df.iterrows():\n",
    "        minutes = row['MIN']\n",
    "        seconds = 0\n",
    "        if (isinstance(minutes, str)):\n",
    "            m, s = minutes.split(\":\")\n",
    "            seconds = int(float(m) * 60 + float(s))\n",
    "        df.loc[idx, 'SEC'] = seconds\n",
    "    return df\n",
    "\n",
    "df_home = calculate_seconds_played(df_home)\n",
    "df_visit = calculate_seconds_played(df_visit)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter dataset features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_home = df_home[['GAME_ID', 'HOME_TEAM_ID', 'PLAYER_ID', 'SEC']]\n",
    "df_visit = df_visit[['GAME_ID', 'VISITOR_TEAM_ID', 'PLAYER_ID', 'SEC']]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot player IDs into rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pivot_players(df, home):\n",
    "    gb = df.groupby(['GAME_ID', f\"{home}_TEAM_ID\"])\n",
    "    game_teams = [gb.get_group(x) for x in gb.groups]\n",
    "    \n",
    "    df_pivoted = pd.DataFrame()\n",
    "    \n",
    "    for x in game_teams:\n",
    "        tmp = x.nlargest(9, 'SEC')\n",
    "        num = 1\n",
    "        for idx, row in tmp.iterrows():\n",
    "            tmp.loc[idx, 'SEC'] = f\"{home}_P{num}\"\n",
    "            num += 1\n",
    "            \n",
    "        tmp.rename(columns={'SEC':'NUM'}, inplace=True)\n",
    "        tmp = pd.pivot_table(tmp, index=['GAME_ID', f\"{home}_TEAM_ID\"], columns='NUM', values='PLAYER_ID', aggfunc= lambda r:r)\n",
    "        df_pivoted = pd.concat([df_pivoted, tmp])\n",
    "    \n",
    "    return df_pivoted\n",
    "    \n",
    "df_home_pivoted = pivot_players(df_home, \"HOME\")\n",
    "df_visit_pivoted = pivot_players(df_visit, \"VISITOR\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save pivoted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_visit_pivoted.to_csv(\"visit_pivoted.csv\")\n",
    "df_home_pivoted.to_csv(\"home_pivoted.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge team sets and also merge game info like data and who won"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "home = pd.read_csv(\"home_pivoted.csv\")\n",
    "visit = pd.read_csv(\"visit_pivoted.csv\")\n",
    "\n",
    "games_filter = games[['GAME_ID', 'DAY', 'WEEK', 'MONTH', 'HOME_TEAM_WINS']]\n",
    "\n",
    "df_data = pd.merge(home, visit, on=['GAME_ID'], how='outer')\n",
    "df_data = pd.merge(df_data, games_filter, on='GAME_ID')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save final processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data.to_csv(\"processed.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Pipeline and Parameter Tuning</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import pandas as pd \n",
    "import os\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Various ways to ignore warnings that clog up the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "os.environ['PYTHONWARNINGS']='ignore'\n",
    "\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read preprocessed data, setup numerical and categorical features and transformers, then build pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                 ColumnTransformer(transformers=[(&#x27;num_transform&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;WEEK&#x27;]),\n",
       "                                                 (&#x27;cat_transform&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;GAME_ID&#x27;, &#x27;HOME_TEAM_ID&#x27;,\n",
       "                                                   &#x27;VISITOR_TEAM_ID&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocesser&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_transform&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;WEEK&#x27;]),\n",
       "                                                 (&#x27;cat_transform&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;GAME_ID&#x27;, &#x27;HOME_TEAM_ID&#x27;,\n",
       "                                                   &#x27;VISITOR_TEAM_ID&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocesser: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_transform&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;WEEK&#x27;]),\n",
       "                                (&#x27;cat_transform&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;GAME_ID&#x27;, &#x27;HOME_TEAM_ID&#x27;,\n",
       "                                  &#x27;VISITOR_TEAM_ID&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_transform</label><div class=\"sk-toggleable__content\"><pre>[&#x27;WEEK&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_transform</label><div class=\"sk-toggleable__content\"><pre>[&#x27;GAME_ID&#x27;, &#x27;HOME_TEAM_ID&#x27;, &#x27;VISITOR_TEAM_ID&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocesser',\n",
       "                 ColumnTransformer(transformers=[('num_transform',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['WEEK']),\n",
       "                                                 ('cat_transform',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['GAME_ID', 'HOME_TEAM_ID',\n",
       "                                                   'VISITOR_TEAM_ID'])])),\n",
       "                ('classifier', LogisticRegression())])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv(\"processed.csv\")\n",
    "features = dataframe.columns.to_list()\n",
    "features.remove('Unnamed: 0')\n",
    "features.remove('HOME_TEAM_WINS')\n",
    "\n",
    "data = dataframe.values\n",
    "X = dataframe[features]\n",
    "y = dataframe.HOME_TEAM_WINS\n",
    "\n",
    "numeric_features = ['WEEK'] #week of year\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"mean\")),\n",
    "    (\"scaler\", StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_features = ['GAME_ID',\n",
    "            'HOME_TEAM_ID',\n",
    "            # 'HOME_P1',\n",
    "            # 'HOME_P2',\n",
    "            # 'HOME_P3',\n",
    "            # 'HOME_P4',\n",
    "            # 'HOME_P5',\n",
    "            # 'HOME_P6',\n",
    "            # 'HOME_P7',\n",
    "            # 'HOME_P8',\n",
    "            # 'HOME_P9',\n",
    "            'VISITOR_TEAM_ID',\n",
    "            # 'VISITOR_P1',\n",
    "            # 'VISITOR_P2',\n",
    "            # 'VISITOR_P3',\n",
    "            # 'VISITOR_P4',\n",
    "            # 'VISITOR_P5',\n",
    "            # 'VISITOR_P6',\n",
    "            # 'VISITOR_P7',\n",
    "            # 'VISITOR_P8',\n",
    "            # 'VISITOR_P9',\n",
    "            ]\n",
    "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[\n",
    "    (\"num_transform\", numeric_transformer, numeric_features),\n",
    "    (\"cat_transform\", categorical_transformer, categorical_features)\n",
    "])\n",
    "pipeline = Pipeline(steps=[(\"preprocesser\", preprocessor), (\"classifier\", LogisticRegression())])\n",
    "pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split test data and perform base run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6124031007751938\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "pipeline.fit(X_train, y_train)\n",
    "score = pipeline.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define parameter grid for grid serach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\n",
    "        \"preprocesser__num_transform__imputer__strategy\": [\"mean\", \"median\"],\n",
    "        \"classifier__C\": [0.1, 1.0, 10.0, 100.0],\n",
    "        \"classifier\": [LogisticRegression()]\n",
    "    },\n",
    "    {\n",
    "        \"preprocesser__num_transform__imputer__strategy\": [\"mean\", \"median\"],\n",
    "        \"classifier__n_estimators\": [10, 100, 1000],\n",
    "        \"classifier\": [RandomForestClassifier()]\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search grid and see best scores!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
      "Best params:\n",
      "{'classifier': LogisticRegression(C=100.0), 'classifier__C': 100.0, 'preprocesser__num_transform__imputer__strategy': 'mean'}\n",
      "Best score in grid search:\n",
      "0.6334059488898199\n",
      "Best logistic regression from grid search:\n",
      "0.6149870801033591\n",
      "\n",
      "Fitting 10 folds for each of 6 candidates, totalling 60 fits\n",
      "Best params:\n",
      "{'classifier': RandomForestClassifier(n_estimators=1000), 'classifier__n_estimators': 1000, 'preprocesser__num_transform__imputer__strategy': 'median'}\n",
      "Best score in grid search:\n",
      "0.6256598240469209\n",
      "Best logistic regression from grid search:\n",
      "0.6175710594315246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for classifier in param_grid:\n",
    "    \n",
    "    grid_search = GridSearchCV(pipeline, classifier, cv=10, verbose=1,n_jobs=-1)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    print(f\"Best params:\")\n",
    "    print(grid_search.best_params_)\n",
    "    print(\"Best score in grid search:\")\n",
    "    print(grid_search.best_score_)\n",
    "    print(\"Best logistic regression from grid search:\")\n",
    "    print(grid_search.score(X_test, y_test))\n",
    "    print()\n",
    "\n",
    "#0.6459948320413437\n",
    "#0.627906976744186"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Best score we saw was 0.6459948320413437, but usually around 0.627 depending on how the test data was split"
   ]
  }
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